Data Warehousing & Etl Pipelines Training Course: Efficient Data Integration For Bi

Introduction

Master the art of building robust data warehousing and ETL pipelines with our comprehensive training course. This program is designed to equip you with the essential skills to create efficient data pipelines for seamless data warehousing and business intelligence (BI). In today's data-driven landscape, the ability to design and implement effective ETL pipelines is crucial for transforming raw data into actionable insights. Our data warehousing training course provides hands-on experience and expert guidance, enabling you to build scalable and reliable data solutions.

This ETL pipelines training delves into the core concepts of data integration, covering topics such as data modeling, ETL design patterns, and performance optimization. You'll gain expertise in using industry-standard tools and techniques to build efficient data pipelines that support informed decision-making. Whether you're a data engineer, BI developer, or data architect, this data warehousing and ETL course will empower you to leverage the full potential of your data for business intelligence.

Target Audience:

• Data Engineers
• Business Intelligence Developers
• Data Architects
• Database Administrators
• ETL Developers
• Data Analysts
• Anyone needing data warehousing and ETL skills

Course Objectives:

• Understand the fundamentals of data warehousing and ETL pipelines.
• Design and implement efficient data pipelines for various data sources.
• Master data modeling techniques for data warehousing.
• Utilize industry-standard ETL tools and technologies.
• Optimize ETL pipeline performance for large datasets.
• Implement data quality checks and validation processes.
• Design and build dimensional data models for BI.
• Automate ETL workflows for efficient data loading.
• Troubleshoot and debug ETL pipeline
• Implement data security and access control in data warehousing.
• Integrate data warehousing solutions with BI tools.
• Explore advanced ETL patterns and techniques.

Apply real world use cases for data warehousing and ETL.

Duration

10 Days

Course content

Module 1: Introduction to Data Warehousing and ETL

• Fundamentals of data warehousing and ETL.
• Understanding the role of ETL pipelines in BI.
• Overview of different data warehousing architectures.
• Setting up a development environment.
• Introduction to ETL tools.

Module 2: Data Modeling for Data Warehousing

• Concepts of dimensional data modeling.
• Designing star and snowflake schemas.
• Implementing fact and dimension tables.
• Utilizing surrogate keys and slowly changing dimensions.
• Best practices for data modeling.

Module 3: ETL Pipeline Design and Development

• Designing ETL pipelines for various data sources.
• Implementing data extraction, transformation, and loading.
• Utilizing ETL design patterns.
• Implementing data quality checks and validation.
• Best practices for ETL development.

Module 4: ETL Tools and Technologies

• Utilizing industry-standard ETL tools (e.g., Talend, Informatica, DataStage).
• Implementing data transformations using SQL and scripting.
• Utilizing cloud-based ETL services (AWS Glue, Azure Data Factory).
• Integrating ETL tools with databases and data lakes.
• Best practices for tool selection.

Module 5: ETL Pipeline Performance Optimization

• Optimizing ETL pipeline performance for large datasets.
• Tuning ETL processes for efficiency.
• Implementing parallel processing and partitioning.
• Utilizing indexing and caching strategies.
• Performance monitoring and analysis.

Module 6: Data Quality and Validation

• Implementing data quality checks and validation processes.
• Utilizing data profiling and cleansing techniques.
• Implementing data reconciliation and auditing.
• Handling data errors and exceptions.
• Best practices for data quality management.

Module 7: Dimensional Modeling for Business Intelligence

• Designing dimensional data models for BI.
• Implementing star and snowflake schemas for reporting.
• Utilizing OLAP cubes and data aggregation.
• Integrating dimensional models with BI tools.
• Best practices for BI data modeling.

Module 8: ETL Workflow Automation

• Automating ETL workflows for efficient data loading.
• Utilizing workflow management tools (e.g., Apache Airflow, Control-M).
• Implementing scheduling and dependency management.
• Automating data quality checks and notifications.
• Best practices for workflow automation.

Module 9: ETL Pipeline Troubleshooting and Debugging

• Troubleshooting and debugging ETL pipeline issues.
• Analyzing ETL logs and error messages.
• Utilizing debugging tools and techniques.
• Identifying and resolving performance bottlenecks.
• Best practices for ETL troubleshooting.

Module 10: Data Security and Access Control

• Implementing data security in data warehousing.
• Utilizing authentication and authorization.
• Implementing data encryption and masking.
• Auditing and compliance in data warehousing.
• Best practices for data security.

Module 11: Integration with Business Intelligence Tools

• Integrating data warehousing solutions with BI tools (e.g., Tableau, Power BI).
• Utilizing data visualization and reporting techniques.
• Implementing data analysis and dashboards.
• Best practices for BI integration.

Module 12: Advanced ETL Patterns and Techniques

• Implementing change data capture (CDC).
• Utilizing data virtualization and data federation.
• Implementing data streaming for real-time ETL.
• Utilizing advanced ETL transformations.
• Best practices for advanced ETL.

Module 13: Data Warehousing in Cloud Environments

• Deploying data warehousing solutions on cloud platforms.
• Utilizing cloud-based data warehousing services (e.g., Redshift, BigQuery).
• Implementing cloud-based ETL pipelines.
• Best practices for cloud data warehousing.

Module 14: Data Governance and Metadata Management

• Implementing data governance policies in data warehousing.
• Utilizing metadata management tools.
• Implementing data lineage and data dictionary.
• Best practices for data governance.

Module 15: Future Trends in Data Warehousing and ETL

• Emerging trends in data warehousing and ETL.
• Utilizing AI and machine learning in ETL processes.
• Implementing data mesh architectures.
• Best practices for future data warehousing.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.